Guatemala Forest Fire Project

Project background

Research Questions:

1) When were fires most abundant in Guatemala?

2) Where are the firest most common in Guatemala?

3) What might be causing this surge in fires?

4) How do the fires affect biodiversity?

5) Is there an area at greater risk of fires and forest loss in Guatemala?

Data Acquisition and Organization

Fire data was acquired for 2019 from NASA. Specifically, data comes from the Near real-time (NRT) Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Active Fire detection product. For more information, please refer to NASA's Earthdata Website: https://earthdata.nasa.gov/earth-observation-data/near-real-time/firms/v1-vnp14imgt#ed-viirs-375m-attributes.

VIIRS (S-NPP) I Band 375 m Active Fire Product NRT (Vector data) data for 2019 was downloaded as a shapefile and visualized in ESRI ArcMap 10.8.1. Forest data comes from the National Institute of Forests in Guatemala (https://data.globalforestwatch.org/datasets/7935041390964af0a09763ff83c30b0e).

Using the VIIRS point data, I extracted values from the Forest Cover raster to determine the kind of forest affected by fires in 2019. Data from this shapefile was then exported as a CSV for ease of access.

On Violin Plots:

There are two KDE plots that are symmetrical along the center line. A white dot represents the median. The thick black line in the center of each violin represents the interquartile range. The lines that extend from the center are the confidence intervals. The violin plot also displays the 95% confidence interval.

Now that we know when fires are most prevalent, let's figure out what kind of forest cover is most affected.

As mentioned above, forest data comes from the National Institute of Forests in Guatemala (https://data.globalforestwatch.org/datasets/7935041390964af0a09763ff83c30b0e). Researchers classified 308 high resolution RapidEye data to create 16 classes of forests for the year 2012.

Using ArcGIS ArcMap, I extracted the raster values representing different forest cover where they interestect with different points representing fires. This allows us to approximate the kind of forest that was predominantly affected by fires in 2019. From the data, we know that the Bosque Latifoliado Denso (Dense Broadleaf Forest) is the most affected by fires in 2019. This is also the type of forest cover most typical in the lowlands of northern Guatemala (for more information see: http://www.reddccadgiz.org/documentos/doc_1170376601.pdf).

Fires and Forests by Departments in Guatemala.

The following section will evaluate the amount of fires in 2019 by department, as well as Forest Cover Loss data from 2001 to 2019. From the data, is clear that the department of the Peten has been the center of fires in 2019. Unfortunately, the Peten has also seen the most Forest Canopy Loss since 2001.

This data was acquired using ArcGIs Pro's Aggregate Point Function. I aggregated the count of fires by department. This data was saved into te fire_aggregate_data_by_dept shapefile and used a geo data frame using Geopandas.

How does the location of fires relate to Forest Cover Loss in the last 19 years?

Data for Forest Cover Loss comes from the University of Maryland Global Forest Change Project: http://earthenginepartners.appspot.com/google.com/science-2013-global-forest

‘Forest Cover Loss’ is defined as a stand-replacement disturbance, or a change from a forest to non-forest state, during the period 2000–2019.

Data was organized in the GTM_dept_ForestLoss shapefile and used as a geo data frame using Geopandas. The SUM column represents the sum of pixels (from 0 to 19) where there has been forest cover loss.

Conclusions:

Forest Fire Data from SIFGUA: http://www.sifgua.org.gt/Incendio.aspx